5.2 AI & Machine Learning Algorithms

• Risk assessment models: Analyze project credibility based on historical trends, team background, and market potential.

• Automated due diligence system: Reduces human error in project evaluation.

• Fraud detection mechanisms: Identify suspicious activities and red flags to protect investors.

  • Model: Random Forest classifier trained on 10K+ scam/non-scam projects.

  • Accuracy: 94% in detecting rug pulls (tested on 2023 data).

• Investor Matching:

  • NLP: BERT models parse investor social profiles for risk appetite.